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Machine learning shows up in products as different as travel search, voice assistants, insurance software, fleet cameras and cloud services. Built In’s September 17, 2026 list offers 38 examples, from large technology companies to specialist vendors. It is best read as a directory of varied use cases—not a measured ranking: the page does not state ranking criteria or define the market it covers.
What the 38-company list shows
The companies do not all sell machine-learning software. Some offer platforms and APIs for developers or enterprises; others use machine learning inside consumer services or industry-specific products. The examples illustrate common applications—recommendation, speech recognition, document processing, forecasting, advertising optimization and computer vision—across travel, finance, healthcare, logistics, retail and operations.
Built In’s opening bullets name OpenAI, Amazon Web Services, Databricks, DataRobot and SoundHound. Its FAQ names further examples, including Unity Interactions, AKASA, Kensho Technologies, Metropolis Technologies, OpenX Technologies and KAYAK. Those placements should not be read as a ranking: the page provides no method for ordering companies by performance or market position. Databricks and Unity Interactions are mentioned in those sections, but are not among the 38 profiles described below.
Companies applying machine learning to consumer services and advertising
Recommendations, travel and matchmaking
- KAYAK uses machine learning and real-time travel data for search and recommendations. Built In also describes an Ask AI tool introduced in 2026.
- Hinge uses machine learning for matchmaking, including its Most Compatible feature.
- Upside personalizes cashback offers for gas, groceries, restaurants and hardware stores.
- Rokt uses machine learning to personalize ecommerce recommendations, upsells and offers.
- Fora applies AI systems to travel-advisor onboarding, payments, hotel bookings and itinerary creation.
Advertising and marketing
- Liftoff uses machine learning for mobile user acquisition, ad monetization and creative optimization.
- OpenX Technologies applies it to programmatic advertising, including inventory quality, audience modeling and campaign optimization.
- System1 operates a digital marketing platform using machine learning, intent data and auctioning technology.
- Smartly uses machine learning to automate digital ad creation and optimize advertising spend.
- Klaviyo uses data science and machine learning for predictive analytics and marketing personalization.
- Snap Inc. uses machine-learning models in Lens Studio to help developers create augmented-reality experiences.
Companies building platforms, models and speech tools
Enterprise and cloud platforms
- DataRobot offers an enterprise AI platform for building, deploying and governing predictive and generative AI models, with automated machine learning and agentic AI capabilities.
- Palantir Technologies describes Foundry and AIP as supporting data preparation, model development, deployment, monitoring and governed enterprise applications.
- Amazon Web Services offers services for conversational AI, fraud detection, document extraction and generative-AI applications.
- Microsoft uses machine learning across consumer, enterprise, developer and Azure cloud services.
- Google embeds machine learning in consumer products and cloud services.
- IBM offers machine-learning tools for automation, predictive analytics, model deployment, risk detection and healthcare analytics.
- OpenAI develops machine-learning models that power ChatGPT and offers enterprise products and models through APIs and cloud platforms.
Speech and language
- SoundHound develops conversational intelligence technology that processes spoken language and user intent. Its Houndify platform lets developers build voice assistants.
- Deepgram provides speech-to-text, text-to-speech and voice-agent APIs for multiple industries. Built In’s profile displays an “over 50,000” processing claim without identifying its unit, so it cannot be interpreted as a complete performance statistic.
- Kensho Technologies uses machine learning and language processing to structure unstructured data and extract insights, including from finance-related text.
Companies automating documents, finance and business operations
Documents and financial workflows
- AKASA applies AI to healthcare revenue-cycle administration and processes clinical and financial documents.
- Canoe extracts and classifies information from alternative-investment documents using AI.
- Hyperscience uses machine learning to extract and classify data from complex documents, including handwritten forms.
- TrueML analyzes consumer behavior and payment patterns to tailor digital debt-collection outreach.
- Upstart uses machine learning in digital lending and credit decision tools.
- Gradient AI applies AI to underwriting and claims workflows in insurance.
- Gusto uses predictive, statistical and machine-learning models in payroll, HR and benefits software.
Data, workflows and prediction
- Monte Carlo is a data-observability company that uses machine learning to identify anomalies.
- Dropbox develops workflow software and is building machine-learning engineering capacity for AI product development.
- Striveworks offers an AIOps platform for dataset preparation, model training and deployment, and monitoring model performance as operating environments change.
- Air Space Intelligence uses simulation and machine-learning models for predictive situational awareness and decision-making in aviation, logistics and military contexts.
Companies using machine learning in physical-world operations
- Metropolis Technologies applies computer vision and machine learning to checkout-free payment experiences at parking and aviation facilities. Built In says its systems identify vehicles and passengers and support dynamic pricing and operational data insights.
- Samsara uses models trained on video footage for fleet dash-camera collision and risk detection, alongside telematics and predictive analytics.
- Agero provides accident management, roadside assistance, warranty programs and electric-vehicle support. Built In reports that it collects more than 60 terabytes of data each year; this is the article’s company-profile claim, not an independently described benchmark.
- AMP uses AI, machine learning and computer vision in recycling sortation systems.
- IDEaS uses machine-learning forecasting and pricing recommendations in hospitality revenue-management software.
How to compare these examples
A useful comparison starts with the job the system performs and who uses the result, rather than treating “uses machine learning” as a product category. These profiles suggest several practical distinctions:
#1 Best Overall
- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
- Application: speech and language, document extraction, recommendations, advertising, prediction or computer vision.
- User or buyer: consumers, developers, enterprise teams or public-sector organizations.
- Delivery: a cloud platform or API, business software, or a consumer service with machine learning built in.
- Industry: examples span travel, marketing, healthcare, financial services, insurance, logistics, hospitality and recycling.
These are useful ways to understand product fit, but the Built In profiles do not provide a shared performance benchmark, comparative scores, prices or selection methodology. They therefore support discovery, not a conclusion about which company has the most effective technology.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Source and scope
This overview follows Built In’s “38 Machine Learning Companies You Should Know,” which says it was updated September 17, 2026 and was accessed October 7, 2026. Company offerings, locations and scale claims can change; consult the company or source page for current details. Read the Built In article.
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